References

• Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017). Machine learning with adversaries: Byzantine tolerant gradient descent. Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA (pp. 118–128).
• Chen, Y., Su, L., & Xu, J. (2017). Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 46(1), 1–25. https://doi.org/10.1145/3308809.3308857
• Ghosh, A., Maity, R. K., Kadhe, S., Mazumdar, A., & Ramchandran, K. (2020). Communication-efficient and byzantine-robust distributed learning with error feedback. arXiv:1911.09721v3.
• Gu, R., Yang, S., & Wu, F. (2019). Distributed machine learning on mobile devices: a survey. arXiv:1909.08329v1, 1–28.
• Lin, S.-B., Guo, X., & Zhou, D.-X. (2017). Distributed learning with regularized least squares. The Journal of Machine Learning Research, 18(49), 3202–3232.
• Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2021). From distributed machine learning to federated learning: a survey. arXiv:2104.14362.
• Shamir, O., Srebro, N., & Zhang, T. (2014). Communication-efficient distributed optimization using an approximate newton-type method. In International conference on machine learning (pp. 1000–1008).
• Su, L., & Vaidya, N. H. (2016). Fault-tolerant multi-agent optimization: optimal iterative distributed algorithms. In Proceedings of the 2016 ACM symposium on principles of distributed computing (pp. 425–434). Association for Computing Machinery.
• Tu, J. Y., Liu, W. D., & Mao, X. J. (2021). Byzantine-robust distributed sparse learning for M-estimation. Machine Learning. https://doi.org/10.1007/s10994-021-06001-x.
• Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & J. S. Rellermeyer (2020). A survey on distributed machine learning. ACM Computing Surveys, 53(2), 1–33. https://doi.org/10.1145/3377454
• Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018). Byzantine-robust distributed learning: Towards optimal statistical rates. Proceedings of the 35th international conference on machine learning, Stockholm, Sweden, PMLR 80 (pp. 5650–5659).
• Zhang, Y., Duchi, J. C., & Wainwright, M. J. (2013). Communication-efficient algorithms for statistical optimization. The Journal of Machine Learning Research, 14, 3321–3363.
• Zhou, X. C., Chang, L., Xu, P. F., & Lv, S. G. (2021). Communication-efficient Byzantine-robust distributed learning with statistical guarantee. arXiv:2103.00373v1.

To cite this article: Shaogao Lv & Xingcai Zhou (2021): Discussion of: ‘A review of distributed
statistical inference’, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2021.2015868
To link to this article: https://doi.org/10.1080/24754269.2021.2015868